The field of affective computing involves the study and development of systems and devices that can recognize, interpret, process, and even simulate human affects. Such systems often rely on statistical models created from data collected by monitoring users and their response to various stimuli. While currently there are some systems for measuring user response to stimuli, the experimental data they rely on is typically generated in a controlled environment like laboratories. In these settings, a small number of short experiments are conducted (typically less than an hour long), in which a small number of peoples' reactions are measured to a set of pre-selected stimuli, such as pictures, video scenes, or music. One drawback of this approach is that the data is collected when the user is in very similar situations (e.g., sitting down in a quiet room). This represents only a very limited and narrow band of the possible situations the user may be in when outside of the laboratory. Furthermore, the user's average (baseline) state and the way the user responds to stimuli can change dramatically depending on the situation the user is in. For example, a user's affective response while working in the office might be quite different from the user's response when relaxing at home, even if exposed to the same stimuli in both situations (e.g., unexpected phone ring). A user's baseline average level of arousal or agitation can change dramatically depending on the situation the user is in, for example, playing basketball with friends, or entering the workplace on Monday morning. Therefore, for affective computing systems to be used successfully in real world applications, it can be beneficial if they are be able to identify different situations, and to recognize in which situations a user is at different times.